Transportation network design problem (NDP) is inherently multi-objective in nature, because it involves a number of stakeholders with different needs. In addition, the decision-making process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper, we develop an alpha reliable NDP model that considers efficiency and equity with demand uncertainty. To solve the multi-objective alpha reliable NDP model, we adopt the simulation-based multi-objective genetic algorithm (SMOGA) solution procedure that explicitly optimizes all objectives under demand uncertainty by simultaneously generating a family of optimal solutions known as the Pareto optimal solution set. Numerical examples are also presented to illustrate the tradeoff between efficiency and equity in the alpha reliable NDP framework.

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dc.description.abstract

Transportation network design problem (NDP) is inherently multi-objective in nature, because it involves a number of stakeholders with different needs. In addition, the decision-making process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper, we develop an alpha reliable NDP model that considers efficiency and equity with demand uncertainty. To solve the multi-objective alpha reliable NDP model, we adopt the simulation-based multi-objective genetic algorithm (SMOGA) solution procedure that explicitly optimizes all objectives under demand uncertainty by simultaneously generating a family of optimal solutions known as the Pareto optimal solution set. Numerical examples are also presented to illustrate the tradeoff between efficiency and equity in the alpha reliable NDP framework.